Deep Learning-Based Metaheuristic Weighted K-Nearest Neighbor Algorithm for the Severity Classification of Breast Cancer

ObjectiveThe most widespread and intrusive cancer type among women is breast cancer. Globally, this type of cancer causes more mortality among women, next to lung cancer. This made the researchers to focus more on developing effective Computer-Aided Detection (CAD) methodologies for the classification of such deadly cancer types. In order to improve the rate of survival and earlier diagnosis, an optimistic research methodology is required in the classification of breast cancer. Consequently, an improved methodology that integrates the principle of deep learning with metaheuristic and classification algorithms is proposed for the severity classification of breast cancer. Hence to enhance the recent findings, an improved CAD methodology is proposed for redressing the healthcare problem.Material and MethodsThe work intends to cast a light-of-research towards classifying the severities present in digital mammogram images. For evaluating the work, the publicly available MIAS, INbreast, and WDBC databases are utilized. The proposed work employs transfer learning for extricating the features. The novelty of the work lies in improving the classification performance of the weighted k-nearest neighbor (wKNN) algorithm using particle swarm optimization (PSO), dragon-fly optimization algorithm (DFOA), and crow-search optimization algorithm (CSOA) as a transformation technique i.e., transforming non-linear input features into minimal linear separable feature vectors.ResultsThe results obtained for the proposed work are compared then with the Gaussian Naïve Bayes and linear Support Vector Machine algorithms, where the highest accuracy for classification is attained for the proposed work (CSOA-wKNN) with 84.35% for MIAS, 83.19% for INbreast, and 97.36% for WDBC datasets respectively.ConclusionThe obtained results reveal that the proposed Computer-Aided-Diagnosis (CAD) tool is robust for the severity classification of breast cancer.     

Deep Learning-Based Multi-Label Tissue Segmentation and Density Assessment from Mammograms

Objectives: Breast cancer is the most commonly diagnosed type of cancer among women and a common cause of cancer-related deaths. Early diagnosis and treatment of breast cancer is critical in disease prognosis. Breast density is known to have a correlation with breast cancer. In recent years, there has been an increasing interest in the investigation of computer-aided methods for early diagnosis of breast cancer. In this study, a new fully-automated deep learning-based cascaded model was proposed for breast density assessment. In the first stage, the segmentation of adipose, fibroglandular, and pectoral muscle tissues from the digitized film mammograms of the Digital Database for Screening Mammography (DDSM) was investigated using various types of U-nets. Features extracted from the breast tissue segmentation predictions were then used to assess breast density in the second stage. Material and methods: 66 and 296 mediolateral oblique mammograms were selected from DDSM dataset for segmentation and breast density assessment systems, respectively. Different U-nets with varying number of layers and filters were implemented and the model having the highest performance was determined. U-net performance was investigated using categorical cross-entropy, Dice, Tversky, Focal Tversky, and logarithmic cosine-hyperbolic Dice loss functions. The performances of U-nets having different types of connections were investigated. The performances of U-nets having pre-trained weights from VGG16, VGG19, and ResNet50 networks in the encoding path were also investigated. Segmentation results were improved by using an image processing pipeline based on morphological operators. Segmentation performance was presented in terms of accuracy, balanced accuracy, intersection over union, and Dice's similarity coefficient (DSC) metrics. The segmentation system predictions were then used to estimate mammographic density using a machine learning pipeline by extracting features related to the fibroglandular tissue percentage. Results: Using ResNet50-U-net on the test data, average DSC scores of 82.71%, 73.39%, and 95.30% were obtained for adipose, fibroglandular, and pectoral muscle tissue segmentation, respectively. The mammogram segmentation results are 3%-12% better than the current state-of-the-art DSC in the literature when considering all of the foreground tissues concurrently. A breast density classification accuracy of 76.01% was achieved on a separate mammogram dataset, which is comparable to the recent studies in the literature. Conclusion: The proposed system can be used for automatic segmentation of mammogram into adipose, fibroglandular, and pectoral muscle tissues. The segmentation model enables the estimation of the fibroglandular-adipose tissue interface, which is recently found to be an important region for breast cancer investigations. The proposed fully-automatic breast density assessment system has a comparable performance to the ones in the literature.     

A computer-aided diagnosis scheme of breast lesion classification using GLGLM and shape features: Combined-view and multi-classifiers

PurposeTo address high false-positive results of FFDM issue, we make the first effort to develop a computer-aided diagnosis (CAD) scheme to analyze and distinguish breast lesions.MethodThe breast lesion regions were first segmented and depicted on FFDM images from 106 patients. In this work, 11 gray-level gap-length matrix texture features and 12 shape features were extracted form craniocaudal view and mediolateral oblique view, and then Student’s t-test, Fisher-score and Relief-F were introduced to select features. We also investigated the effect of three factors, i.e., discretisation, selection methods and classifier methods, of the classification performance via analysis of variance. Finally, a classification model was constructed. Spearman’s correlation coefficient analysis was conducted to assess the internal relevance of features.ResultsThe proposed scheme using Student’s t-test achieved an area under the receiver operating characteristic curve (AUC) value of 0.923 at 512 bins. The AUC values are 0.884, 0.867, 0.874 and 0.901 for the low gray-level gaps emphasis (LGGE), solidity, extent, and the combined set, respectively. Solidity and extent depicts the correlation coefficient of 0.86 (P < 0.05).ConclusionsWe present a new CAD scheme based on the contribution of the significant factors. The experimental results demonstrate that the presented scheme can be used to successfully distinguish breast carcinoma lesions and benign fibroadenoma lesions in our FFDM dataset and the MIAS dataset, which may provide a CAD method to assist radiologists in diagnosing and interpreting screening mammograms. Moreover, we found that LGGE, solidity and extent features show great potential for breast lesion classification.     

Optimized Two-Stage Ensemble Model for Mammography Mass Recognition

ObjectivesMammography mass recognition is considered as a very challenge pattern recognition problem due to the high similarity between normal and abnormal masses. Therefore, the main objective of this study is to develop an efficient and optimized two-stage recognition model to tackle this recognition task.Material and methodsBasically, the developed recognition model combines an ensemble of linear Support Vector Machine (SVM) classifiers with a Reinforcement Learning-based Memetic Particle Swarm Optimizer (RLMPSO) as RLMPSO-SVM recognition model. RLMPSO is used to construct a two-stage of an ensemble of linear SVM classifiers by performing simultaneous SVM parameters tuning, features selection, and training instances selection. The first stage of RLMPSO-SVM recognition model is responsible about recognizing the input ROI mammography masses as normal or abnormal mass pattern. Meanwhile, the second stage of RLMPSO-SVM model used to perform further recognition for abnormal ROIs as malignant or benign masses. In order to evaluate the effectiveness of RLMPSO-SVM, a total of 1187 normal ROIs, 111 malignant ROIs, and 135 benign ROIs were randomly selected from DDSM database images.ResultsReported results indicated that RLMPSO-SVM model was able to achieve performances of 97.57% sensitivity rate with 97.86% specificity rate for normal vs. abnormal recognition cases. For malignant vs. benign recognition performance it was reported of 97.81% sensitivity rate with 96.92% specificity rate.ConclusionReported results indicated that RLMPSO-SVM recognition model is an effective tool that could assist the radiologist during the diagnosis of the presented abnormalities in mammography images. The outcomes indicated that RLMPSO-SVM significantly outperformed various SVM-based models as well as other variants of computational intelligence models including multi-layer perceptron, naive Bayes classifier, and k-nearest neighbor.     

Wavelet-Based 3D Reconstruction of Microcalcification Clusters from Two Mammographic Views: New Evidence That Fractal Tumors Are Malignant and Euclidean Tumors Are Benign

The 2D Wavelet-Transform Modulus Maxima (WTMM) method was used to detect microcalcifications (MC) in human breast tissue seen in mammograms and to characterize the fractal geometry of benign and malignant MC clusters. This was done in the context of a preliminary analysis of a small dataset, via a novel way to partition the wavelet-transform space-scale skeleton. For the first time, the estimated 3D fractal structure of a breast lesion was inferred by pairing the information from two separate 2D projected mammographic views of the same breast, i.e. the cranial-caudal (CC) and mediolateral-oblique (MLO) views. As a novelty, we define the “CC-MLO fractal dimension plot”, where a “fractal zone” and “Euclidean zones” (non-fractal) are defined. 118 images (59 cases, 25 malignant and 34 benign) obtained from a digital databank of mammograms with known radiologist diagnostics were analyzed to determine which cases would be plotted in the fractal zone and which cases would fall in the Euclidean zones. 92% of malignant breast lesions studied (23 out of 25 cases) were in the fractal zone while 88% of the benign lesions were in the Euclidean zones (30 out of 34 cases). Furthermore, a Bayesian statistical analysis shows that, with 95% credibility, the probability that fractal breast lesions are malignant is between 74% and 98%. Alternatively, with 95% credibility, the probability that Euclidean breast lesions are benign is between 76% and 96%. These results support the notion that the fractal structure of malignant tumors is more likely to be associated with an invasive behavior into the surrounding tissue compared to the less invasive, Euclidean structure of benign tumors. Finally, based on indirect 3D reconstructions from the 2D views, we conjecture that all breast tumors considered in this study, benign and malignant, fractal or Euclidean, restrict their growth to 2-dimensional manifolds within the breast tissue.     

超声造影联合超声弹性成像组织弥散定量分析在乳腺癌诊断中的应用

摘要 目的：探讨超声造影联合超声弹性成像组织弥散定量分析在乳腺癌诊断中的应用价值。方法：2019年1月至2020年5月选择在本院诊治的乳腺肿瘤患者148例,所有患者都给予超声造影联合超声弹性成像组织弥散定量分析,记录影像学特征。结果：在148例患者中,病理诊断为乳腺癌32例(恶性组),良性乳腺肿瘤116例(良性组)。良性组与恶性组的超声病灶形状、边缘、回声、微钙化等特征对比差异有统计学意义(P<0.05)。恶性组的超声造影增强模式、强度与良性组对比差异都有统计学意义(P<0.05)。恶性组的造影灌注参数曲线下面积(Area under the curve,AUC)、峰值强度(Peak intensity,PI)、上升支斜率(Wash in slope,WIS)值都高于良性组,达峰时间(Time To Peak,TTP)值低于良性组,对比差异都有统计学意义(P<0.05)。恶性组的组织弥散定量参数蓝色区域面积百分比(area ratio,%AREA)低于良性组,标准差(standard deviation,SD)、应变均值(mean,MEAN)值高于良性组,对比差异都有统计学意义(P<0.05)。结论：超声造影联合超声弹性成像组织弥散定量分析在乳腺癌诊断中的应用作为一种经济快捷、实时无创、重复性好的检查方法,能够定量评估乳腺癌的影像学特征,可为乳腺癌的临床治疗提供更多有价值的信息。     

彩色多普勒超声应用于乳腺癌诊断及其新辅助化疗疗效评价的临床价值研究

目的:探讨彩色多普勒超声对乳腺良恶性肿瘤的鉴别诊断价值以及其对乳腺癌患者新辅助化疗疗效的评估价值。方法:选取2017年1月到2018年11月期间在我院接受治疗的乳腺癌患者88例作为乳腺癌组,另选取同期在我院接受治疗的乳腺良性肿瘤患者60例作为良性对照组,良性对照组在治疗前,乳腺癌患者在化疗前后采用彩色多普勒超声进行检查,记录所有患者的二维超声表现、彩色多普勒超声表现。结果:乳腺癌组的形态不规则、边界不清晰、内部回声不均匀、后方回声异常的比例均高于良性对照组,差异有统计学意义(P<0.05),两组患者的血流分级分布情况整体比较差异有统计学意义(P<0.05),乳腺癌组的血流阻力指数(RI)高于良性对照组(P<0.05)。化疗后,治疗有效组的乳腺肿瘤体积小于治疗无效组,治疗有效组的形态不规则、边界不清晰、内部回声不均匀、后方回声异常的比例低于治疗无效组(P<0.05),治疗有效组的血流分级分布情况及RI与治疗无效组比较差异亦有统计学意义(P<0.05)。结论:彩色多普勒超声对乳腺良恶性肿瘤具有较高的鉴别诊断价值,同时也可用于乳腺癌患者新辅助化疗疗效的评估。     

Mass type-specific sparse representation for mass classification in computer-aided detection on mammograms

Background

Breast cancer is the leading cause of both incidence and mortality in women population. For this reason, much research effort has been devoted to develop Computer-Aided Detection (CAD) systems for early detection of the breast cancers on mammograms. In this paper, we propose a new and novel dictionary configuration underpinning sparse representation based classification (SRC). The key idea of the proposed algorithm is to improve the sparsity in terms of mass margins for the purpose of improving classification performance in CAD systems.

Methods

The aim of the proposed SRC framework is to construct separate dictionaries according to the types of mass margins. The underlying idea behind our method is that the separated dictionaries can enhance the sparsity of mass class (true-positive), leading to an improved performance for differentiating mammographic masses from normal tissues (false-positive). When a mass sample is given for classification, the sparse solutions based on corresponding dictionaries are separately solved and combined at score level. Experiments have been performed on both database (DB) named as Digital Database for Screening Mammography (DDSM) and clinical Full Field Digital Mammogram (FFDM) DBs. In our experiments, sparsity concentration in the true class (SCTC) and area under the Receiver operating characteristic (ROC) curve (AUC) were measured for the comparison between the proposed method and a conventional single dictionary based approach. In addition, a support vector machine (SVM) was used for comparing our method with state-of-the-arts classifier extensively used for mass classification.

Results

Comparing with the conventional single dictionary configuration, the proposed approach is able to improve SCTC of up to 13.9% and 23.6% on DDSM and FFDM DBs, respectively. Moreover, the proposed method is able to improve AUC with 8.2% and 22.1% on DDSM and FFDM DBs, respectively. Comparing to SVM classifier, the proposed method improves AUC with 2.9% and 11.6% on DDSM and FFDM DBs, respectively.

Conclusions

The proposed dictionary configuration is found to well improve the sparsity of dictionaries, resulting in an enhanced classification performance. Moreover, the results show that the proposed method is better than conventional SVM classifier for classifying breast masses subject to various margins from normal tissues.

     

剪切波弹性成像定量参数对乳腺肿块良恶性的鉴别价值及其与组织Ki-67和C-erbB-2表达的关系研究

摘要 目的：探讨剪切波弹性成像（SWE）定量参数对乳腺肿块良恶性的鉴别价值,分析其与组织Ki-67和表皮生长因子受体2（C-erbB-2）表达的关系。方法：选择2021年1月至2022年1月于湖南省妇幼保健院超声医学科行乳腺SWE检查的106例乳腺肿块患者,根据术后或组织活检的病理检查结果分为恶性组59例和良性组47例。对比恶性组和良性组SWE参数的差异以及不同Ki-67和C-erbB-2表达乳腺癌病灶SWE参数的差异。Spearman分析乳腺癌病灶SWE参数与乳腺癌组织Ki-67和C-erbB-2表达的相关性。绘制受试者工作特征（ROC）曲线分析乳腺病灶SWE参数鉴别乳腺肿块良恶性的价值。结果：恶性组AE-max、Shell1 E _max 、Shell2 E _max 、Shell3 E _max 高于良性组（P＜0.05）。联合AE-max、Shell1 E _max 、Shell2 E _max 、Shell3 E _max 鉴别乳腺肿块良恶性的曲线下面积为0.841,高于单独参数鉴别的0.657、0.599、0.642、0.609（P＜0.05）。Ki-67阳性组、C-erbB-2阳性组AE-max、Shell1 E _max 、Shell2 E _max 、Shell3 E _max 高于阴性组（P＜0.05）。乳腺癌病灶AE-max、Shell1 E _max 、Shell2 E _max 、Shell3 E _max 与Ki-67和C-erbB-2表达均呈正相关（P＜0.05）。结论：乳腺恶性肿块与良性肿块的SWE参数存在明显差异,且与乳腺癌组织的Ki-67、C-erbB-2阳性表达有关,乳腺SWE检查有助于鉴别乳腺肿块良恶性。     

Risk stratification of women with false-positive test results in mammography screening based on mammographic morphology and density: A case control study

BackgroundThe long-term risk of breast cancer is increased in women with false-positive (FP) mammography screening results. We investigated whether mammographic morphology and/or density can be used to stratify these women according to their risk of future breast cancerMethodsWe undertook a case-control study nested in the population-based screening programme in Copenhagen, Denmark. We included 288 cases and 288 controls based on a cohort of 4743 women with at least one FP-test result in 1991–2005 who were followed up until 17 April 2008. Film-based mammograms were assessed using the Breast Imaging-Reporting and Data System (BI-RADS) density classification, the Tabár classification, and two automated techniques quantifying percentage mammographic density (PMD) and mammographic texture (MTR), respectively. The association with breast cancer was estimated using binary logistic regression calculating Odds Ratios (ORs) and the area under the receiver operating characteristic (ROC) curves (AUCs) adjusted for birth year and age and invitation round at the FP-screenResultsSignificantly increased ORs were seen for BI-RADS D(density)2-D4 (OR 1.94; 1.30-2.91, 2.36; 1.51-3.70 and 4.01; 1.67-9.62, respectively), Tabár’s P(pattern)IV (OR 1.83; 1.16-2.89), PMD Q(quartile)2-Q4 (OR 1.71; 1.02-2.88, 1.97; 1.16-3.35 and 2.43; 1.41-4.19, respectively) and MTR Q4 (1.97; 1.12-3.46) using the lowest/fattiest category as referenceConclusionAll four methods, capturing either mammographic morphology or density, could segregate women with FP-screening results according to their risk of future breast cancer − using already available screening mammograms. Our findings need validation on digital mammograms, but may inform potential future risk stratification and tailored screening strategies     

钼靶和超声检查在乳腺癌临床诊断的准确性的比较分析

摘要 目的：比较与分析钼靶和超声检查在乳腺癌临床诊断的准确性。方法：2018年8月到2021年1月选择在本院进行诊治的乳腺肿瘤患者110例作为研究对象,所有患者都给予钼靶和超声检查,记录影像学特征并判断诊断价值。结果：在110例患者中,病理诊断为乳腺良性肿瘤76例、乳腺癌34例。恶性组钼靶的分叶征、钙化、大角征、毛刺征等比例高于良性组,病灶大小也高于良性组(P<0.05)。恶性组超声的形态不规则、边缘不光整、高回声晕、回声衰减、微钙化等比例高于良性组(P<0.05)。钼靶乳腺影像报告及数据系统(Breast imaging report and data system,BI-RADS)判断为乳腺良性肿瘤72例,乳腺癌38例;超声BI-RADS判断为乳腺良性肿瘤75例,乳腺癌35例,钼靶鉴别诊断乳腺癌的敏感性为93.4%,特异性为97.1%,准确性为94.5%;超声鉴别诊断乳腺癌的敏感性为98.7%,特异性为100.0%,准确性为99.1%。多因素logistic回归分析显示病灶大小、分叶征、回声衰减、毛刺征为导致误诊的重要因素(P<0.05)。结论：乳腺癌在钼靶和超声检查中都有明显的征象特征,超声诊断的准确性更高,病灶大小、分叶征、回声衰减、毛刺征为影响诊断效果的很重要因素。     

乳腺良性肿块与乳腺癌患者的超声弹性成像对比

目的:对比乳腺良性肿块与乳腺癌患者的超声弹性成像,明确超声弹性成像的应用价值。方法:选取2014年5月-2016年1月我院乳腺肿块患者128人次共146例肿块,根据病理结果分为乳腺良性肿块和乳腺癌,比较超声弹性成像与病理结果。结果:128个患者共计肿块146例,99例结节为良性肿块,其中32例为乳腺纤维腺瘤,29例为乳腺增生结节,20例为乳腺脂肪瘤,6例为乳腺血管脂肪瘤,4例为乳腺导管腺瘤,8例为乳腺导管内乳头状瘤;47例肿块为恶性,其中37例肿块为浸润性导管癌,9例肿块为粘液腺癌,1例肿块为硬癌。乳腺良性肿块患者81人次共99例,其中1分43例(43.43%),2分34例(34.34%),3分18例(18.18%),4分4例(4.04%);乳腺癌患者47例,其中3分9例(19.15%),4分20例(42.55%),5分18例(38.30%)。超声弹性成像鉴别乳腺良性肿块与乳腺癌的灵敏度为95.96%,特异性为80.85%,准确度为91.10%,阴性预测值为90.48%,阳性预测值为91.35%。结论:超声弹性成像鉴别乳腺良性肿块与乳腺癌的灵敏度高达95.96%,具有较高准确度,可辅助诊断乳腺疾病。     

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion.Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.     

Cardiovascular Disorder Severity Detection Using Myocardial Anatomic Features Based Optimized Extreme Learning Machine Approach

ObjectivesThis study focuses on integration of anatomical left ventricle myocardium features and optimized extreme learning machine (ELM) for discrimination of subjects with normal, mild, moderate and severe abnormal ejection fraction (EF). The physiological alterations in myocardium have diagnostic relevance to the etiology of cardiovascular diseases (CVD) with reduced EF.Materials and MethodsThis assessment is carried out on cardiovascular magnetic resonance (CMR) images of 104 subjects available in Kaggle Second Annual Data Science Bowl. The Segment CMR framework is used to segment myocardium from cardiac MR images, and it is subdivided into 16 sectors. 86 clinically significant anatomical features are extracted and subjected to ELM framework. Regularization coefficient and hidden neurons influence the prediction accuracy of ELM. The optimal value for these parameters is achieved with the butterfly optimizer (BO). A comparative study of BOELM framework with different activation functions and feature set has been conducted.ResultsAmong the individual feature set, myocardial volume at ED gives a better classification accuracy of 83.3% compared to others. Further, the given BOELM framework is able to provide higher multi-class accuracy of 95.2% with the entire feature set than ELM. Better discrimination of healthy and moderate abnormal subjects is achieved than other sub groups.ConclusionThe combined anatomical sector wise myocardial features assisted BOELM is able to predict the severity levels of CVDs. Thus, this study supports the radiologists in the mass diagnosis of cardiac disorder.     

3.0 T磁共振扩散加权成像在乳腺良恶性病变鉴别中的价值及较优b值下ADC值与预后因子相关性研究

摘要 目的：研究3.0 T磁共振扩散加权成像在乳腺良恶性病变鉴别中的价值及较优b值下ADC值与预后因子的相关性。方法：选取2017年11月～2019年11月于我院接受诊治的乳腺病变患者50例进行研究,将其按照良恶性差异分成恶性组40例与良性组10例,另取同期于我院体检的健康志愿者50例作为对照组。对所有人员均进行3.0 T磁共振扩散加权成像,比较不同b值下ADC值在不同乳腺组织中的差异,比较不同b值下诊断乳腺良恶性病变的效能,分析较优b值下ADC值和乳腺癌患者各项预后因子的相关性。结果：对照组、良性组、恶性组在不同b值下的ADC值均呈逐渐降低趋势（P＜0.05）;对照组、良性组、恶性组b值为1000 s/mm²下的ADC值均低于b值为600 s/mm²（P＜0.05）。b值为1000 s/mm²时诊断乳腺恶性病变的敏感度、特异度、准确度分别为92.50%、100.00%、94.00%,高于b值为600 s/mm²的70.00%、60.00%、68.00%（P＜0.05）。b值为1000 s/mm²下雌激素受体、孕激素受阳性患者的ADC值低于阴性患者,而人类表皮生长因子受体2阳性患者的ADC值高于阴性患者（P＜0.05）。经Spearman相关性分析可得,b值为1000 s/mm²下ADC值与雌激素受体、孕激素受体阳性表达均呈负相关关系,而与人类表皮生长因子受体2阳性表达呈正相关关系（P＜0.05）。结论：3.0 T磁共振扩散加权成像在乳腺良恶性病变鉴别中的价值较高,且以b值为1000 s/mm²的诊断能效较优。此外,b值下ADC值和乳腺癌部分预后因子表达状态密切相关。     

Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods

PurposeAccurate and timely diagnosis of breast cancer is extremely important because of its high incidence and high morbidity. Early diagnosis of breast cancer through screening can improve overall prognosis. Currently, biopsy remains as the gold standard for tumor pathological confirmation. Development of diagnostic imaging techniques for rapid and accurate characterization of breast lesions is required. We aim to evaluate the usefulness of texture-derivate features of QUS spectral parametric images for non-invasive characterization of breast lesions.MethodsQUS Spectroscopy was used to determine parametric images of mid-band fit (MBF), spectral slope (SS), spectral intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) in 204 patients with suspicious breast lesions. Subsequently, texture analysis techniques were used to generate texture maps from parametric images to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS parameters, texture-parameters and texture-derivate parameters were determined from both tumor core and a 5-mm tumor margin and were used in comparison to histopathological analysis in order to develop a diagnostic model for classifying breast lesions as either benign or malignant. Both leave-one-out and hold-out cross-validations were used to evaluate the performance of the diagnostic model. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated.ResultsCore and margin information using the SVM-RBF attained the best classification performance of 90% sensitivity, 92% specificity, 91% accuracy, and 0.93 AUC utilizing QUS parameters and their texture derivatives, evaluated using leave-one-out cross-validation. Implementation of hold-out cross-validation using combination of both core and margin information and SVM-RBF achieved average accuracy and AUC of 88% and 0.92, respectively.ConclusionsQUS-based framework and derivative texture methods enable accurate classification of breast lesions. Evaluation of the proposed technique on a large cohort using hold-out cross-validation demonstrates its robustness and its generalization.     

乳腺良恶性病变剪切波弹性成像的典型表现及其鉴别诊断价值

目的:观察乳腺良恶性病变的剪切波弹性成像(SWE)的典型表现,探讨SWE对乳腺良恶性病变的鉴别诊断价值。方法:选取2017年6月～2019年6月我院收治的162例行SWE检查的乳腺肿块患者,经组织活检或病理证实良性肿块105例(良性组)、恶性肿块57例(恶性组)。对比良、恶性组SWE的典型表现、SWE参数[最大值(Emax)、最小值(Emin)、平均值(Emean)、标准差(SD)、病灶与邻近脂肪弹性比值(SWE-Ratio)]的差异,分析SWE鉴别诊断乳腺良恶性病变的价值。结果:恶性组乳腺肿块"硬边征"检出率、Ⅲ型～Ⅴ型弹性图像检出率、Emax、Emean、SD、SWE-Ratio均高于良性组(P0.05),Emin低于良性组(P0.05)。Logistic多元回归分析结果显示,"硬边征"、Emax、Emean、SWE-Ratio与病理诊断乳腺肿块性质独立相关(P0.05)。受试者工作特征(ROC)曲线分析结果显示,"硬边征"、Emax、Emean、SWE-Ratio鉴别诊断乳腺良恶性病变的曲线下面积(AUC)分别为0.923、0.686、0.873、0.879。结论:SWE是诊断乳腺良恶性病变的有效影像手段,SWE的"硬边征"、SWE-Ratio、Emean对乳腺良恶性病变具有较高的鉴别价值。     

基于X线图像的膝关节周围原发性骨肿瘤辅助诊断的机器学习模型研究

摘要 目的：开发机器学习模型,并评估其在膝关节周围原发性骨肿瘤诊断方面的准确性。方法：本文将深度卷积神经网络（DCNN）这一深度学习方法应用于膝关节X线图像的影像组学分析,探讨其辅助诊断膝关节周围原发性骨肿瘤的临床价值。结果：该深度学习模型在区分正常与肿瘤影像方面展现出优异的诊断准确性,使用DCNN模型进行5轮测试的总体准确性为（99.8±0.4）%,而阳性预测值和阴性预测值分别为（100.0±0.0）%和（99.6±0.8）%,各个数据集的曲线下面积（AUC）分别为0.99、1.00、1.00、1.0和1.0,平均AUC为（0.998±0.004）;进一步使用DCNN模型进行了10轮测试显示其在区分良性与恶性骨肿瘤方面的总体准确性为（71.2±1.6）%,且达到了强阳性预测值（91.9±8.5）%,各个数据集的AUC分别为0.63、0.63、0.58、0.69、0.55、0.63、0.54、0.57、0.73、0.63,平均AUC为（0.62±0.06）。结论：本文是首个将人工智能技术应用于骨肿瘤诊断的X线图像影像组学分析方面的研究,人工智能影像组学模型能够帮助医生自动地快速筛查骨肿瘤,确定良性或恶性肿瘤时,阳性预测值较高。     

Radiotransparency of the triglyceride mammary prosthesis: a quantitative analysis with mastectomy specimens.

Breast cancer is the most frequent malignant tumor in women. It is estimated that 10 percent of women will present with a breast cancer during their lives. It is well known that mammography is the best technique for the early diagnosis of nonpalpable tumors, thus improving life expectancy. However, mammary prostheses may hide between 23 and 82 percent of the normal mammary tissue in mammography, and thus may delay the diagnosis of malignant mammary tumors, making prognosis worse. To solve this problem, oil-filled prostheses have been developed. In this study, 14 mastectomy specimens were used. Mammograms of the tissue pieces alone and also mammograms of the tissue pieces covering a 270-cc Trilucent prosthesis were used to verify whether the prosthesis allows observation of malignant signs in mammography. Mammograms were evaluated by an independent experienced radiologist. The following variables were studied: number of mammograms necessary to examine each specimen; kilovoltage and milliamperage necessary for each mammogram; number of microcalcification groups (malignant); number of macroscopic calcifications (benign); and rarefaction areas that were suspected for malignancy. All of these variables were measured for both mammograms for which the mastectomy specimens were covering and those for which the specimens were not covering the prothesis. Finally, the kilovoltage and milliamperage increases necessary to visualize the mammograms with mastectomy specimens covering the prosthesis were determined. Statistical analysis of the results obtained was performed. There were no significant differences in the number of mammograms (p = 0.391), the number of microcalcifications (p = 0.890), the number of macrocalcifications (p = 0.239), and finally in the presence of rarefaction areas (p = 1.000) observed in the mammograms in specimens either covering or not covering the prosthesis. However, there were significant differences (p < 0.001) between the kilovoltage and milliamperage applied to carry out the mammograms of specimens with and without the prosthesis. Thus, Trilucent prostheses allow visualization of the microscopic and macroscopic calcifications as well as rarefaction areas in mammograms. However, these mammograms required a higher kilovoltage and milliamperage compared with specimens not covering the prosthesis. To explore the whole gland, it might be necessary to perform two series of mammograms: one to detect the area shadowed by the prosthesis and one to observe the rest of the peripheral gland.     

Plasma hepcidin in early-stage breast cancer patients: no relationship with interleukin-6, erythropoietin and erythroferrone

Objective: Hepcidin-25 production is stimulated by systemic inflammation, and it interferes with iron utilization, leading to anemia. This study aimed to investigate the relationships between the plasma levels of hepcidin, interleukin-6 (IL-6), erythropoietin (EPO) and erythroferrone (ERFE) in patients with benign breast disease or cancer. Methods: Plasma samples from a cohort of 131 patients (47 with benign breast disease and 84 with breast cancer) were subjected to the evaluation of hepcidin, IL-6, EPO and ERFE using SELDI-TOF-MS or immunoassays. Results: An elevated hepcidin was observed in malignant breast tumors compared to benign ones. No correlation was observed between hepcidin and IL-6, EPO or ERFE. Conclusion: Since the study included a cohort of patients (87%) with breast cancers smaller than 2 cm, these results may support our previous evidence about the potential role of hepcidin in breast cancer disease.